Face detection based on skin texture recognition may be employed in a variety of applications including biometrics, surveillance and access control, realistic rendering for computer graphics, robust face models for computer vision, computer-assisted diagnosis for dermatology, and so forth. Quantitative characterization of skin appearance is an important, but difficult task. The skin surface is a detailed landscape, with complex geometry and local optical properties. Additionally, skin features depend on many variables such as body location (e.g., forehead, cheek), object parameters (e.g., age, gender), and imaging parameters (e.g., lighting, camera). The skin appearance may be strongly affected by the direction from which it is viewed and illuminated.
Facial features may be measured at a distance and without cooperation or knowledge of an individual. Conventional face recognition techniques often utilize a two-dimensional image (e.g. digital photographs) of a face acquired in uncontrolled environments to create a three-dimensional representation of the face. One of the problems with such techniques stems from the illumination and pose variations of the face, which can distort the projection of a three-dimensional face on a resulting two-dimensional image. Illumination variation may affect correlation based associated with image comparison, as the pixel values vary with varying illumination. Pose variation occurs as the projection in the image may change dramatically as the object rotates. Unfortunately, such prior art face recognition techniques are neither reliable nor accurate enough for arbitrary lighting and applications in unconstrained environments. Image variations due to such factors rendering the face detection and recognition tasks are more difficult.
Based on the foregoing, it is believed that a need exists for an improved three-dimensional multilayer skin texture recognition technology. A need also exists for extracting skin signature to detect faces irrespective of illuminations, facial poses, orientations, and facial expressions, as described in greater detail herein.